The scientists combined two different types of kernel functions to improve face recognition accuracy. By using a mix of Power and Gaussian kernel functions, they were able to better capture the unique features of human faces. Their method, called multiple kernel sparse representation classification, outperformed traditional single kernel methods in recognizing faces. This approach helps computers better understand and identify human faces in images.